“Carotenoid analysis of cassava genotypes roots (Manihot esculenta Crantz) cultivated in southern Brazil using chemometric tools” Author: “Moresco, R.(2014)” Date: “Thursday, January 22, 2015”
R script for Analysis of HPLC and UV-Visible Spectrophotometric Data
Reading data and metadata
setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
source("http://bioconductor.org/biocLite.R")
source("scripts/init.R")
uv.cassava.metadata.file = "Datasets/CassavaCultivars/UVV/metadata/cass_uv_metadata.csv"
uv.cassava.data.file = "Datasets/CassavaCultivars/UVV/data/uvv-cassava.csv"
label.x = "wavelength(nm)"
label.val = "absorbance"
uv.cassava.ds = read.dataset.csv(uv.cassava.data.file, uv.cassava.metadata.file,
description = "UV data for cassava", type = "uvv-spectra", format = "col",
label.x = label.x, label.values = label.val)Preliminary Inspection of Data
sum.dataset(uv.cassava.ds)## Dataset summary:
## Valid dataset
## Description: UV data for cassava
## Type of data: uvv-spectra
## Number of samples: 30
## Number of data points 501
## Number of metadata variables: 3
## Label of x-axis values: wavelength(nm)
## Label of data points: absorbance
## Number of missing values in data: 0
## Mean of data values: 0.1605
## Median of data values: 0.01487
## Standard deviation: 0.4252
## Range of values: -0.1068 2.722
## Quantiles:
## 0% 25% 50% 75% 100%
## -0.1067503 -0.0005422 0.0148732 0.0888727 2.7218540Get metadata
get.metadata(uv.cassava.ds)## varieties colors replicates
## Apronta mesa_1 Apronta.mesa cream 1
## Apronta mesa_2 Apronta.mesa cream 2
## Apronta mesa_3 Apronta.mesa cream 3
## Pioneira_1 Pioneira yellow 1
## Pioneira_2 Pioneira yellow 2
## Pioneira_3 Pioneira yellow 3
## Oriental_1 Oriental cream 1
## Oriental_2 Oriental cream 2
## Oriental_3 Oriental cream 3
## Amarela_1 Amarela yellow 1
## Amarela_2 Amarela yellow 2
## Amarela_3 Amarela yellow 3
## Catarina_1 Catarina yellow 1
## Catarina_2 Catarina yellow 2
## Catarina_3 Catarina yellow 3
## IAC 576-70_1 IAC.576.70 yellow 1
## IAC 576-70_2 IAC.576.70 yellow 2
## IAC 576-70_3 IAC.576.70 yellow 3
## Salezio_1 Salezio cream 1
## Salezio_2 Salezio cream 2
## Salezio_3 Salezio cream 3
## Estacao_1 Estacao cream 1
## Estacao_2 Estacao cream 2
## Estacao_3 Estacao cream 3
## Videira_1 Videira cream 1
## Videira_2 Videira cream 2
## Videira_3 Videira cream 3
## Rosada_1 Rosada red 1
## Rosada_2 Rosada red 2
## Rosada_3 Rosada red 3USING FULL UV-VISIBLE DATA (200-700 nm)
plot.spectra(uv.cassava.ds,"varieties")
Data Pre-Processing
Smoothing and baseline correction
uv.cassava.wavelens = get.x.values.as.num(uv.cassava.ds)
x.axis.sm = seq(min(uv.cassava.wavelens), max(uv.cassava.wavelens),10)
uv.cassava.smooth = smoothing.interpolation(uv.cassava.ds, method = "loess", x.axis = x.axis.sm)
plot.spectra(uv.cassava.smooth, "varieties")
uv.cassava.bg = data.correction(uv.cassava.smooth,"background")
uv.cassava.offset = data.correction(uv.cassava.bg, "offset")
uv.cassava.baseline = data.correction(uv.cassava.offset, "baseline")
sum.dataset(uv.cassava.baseline)## Dataset summary:
## Valid dataset
## Description: UV data for cassava-smoothed with hyperSpec spc.loess; background correction; offset correction; baseline correction
## Type of data: undefined
## Number of samples: 30
## Number of data points 51
## Number of metadata variables: 3
## Label of x-axis values: wavelength(nm)
## Label of data points: absorbance
## Number of missing values in data: 0
## Mean of data values: 0.08889
## Median of data values: 0.02441
## Standard deviation: 0.1923
## Range of values: -0.0002181 1.29
## Quantiles:
## 0% 25% 50% 75% 100%
## -0.0002181 0.0076378 0.0244131 0.0764408 1.2895338plot.spectra(uv.cassava.baseline, "varieties")
UNIVARIATE ANALYSIS
uv.cassava.baseline.anova = univariate.analysis(uv.cassava.baseline, type = "anova", "varieties")
uv.cassava.baseline.anova[1:10,]## pvalues logs fdr
## 500 1.073e-18 17.97 5.470e-17
## 470 4.485e-18 17.35 1.144e-16
## 490 1.394e-17 16.86 2.083e-16
## 460 1.642e-17 16.78 2.083e-16
## 510 2.043e-17 16.69 2.083e-16
## 480 6.334e-17 16.20 5.384e-16
## 290 7.563e-17 16.12 5.510e-16
## 440 3.299e-16 15.48 2.103e-15
## 450 4.829e-16 15.32 2.736e-15
## 300 1.322e-15 14.88 6.740e-15
## tukey
## 500 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 470 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 490 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada; Videira-Salezio
## 460 Rosada-Amarela; Catarina-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 510 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 480 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Salezio-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 290 Apronta.mesa-Amarela; IAC.576.70-Amarela; Oriental-Amarela; Pioneira-Amarela; Salezio-Amarela; Videira-Amarela; Catarina-Apronta.mesa; Estacao-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Videira-Apronta.mesa; IAC.576.70-Catarina; Oriental-Catarina; Pioneira-Catarina; Salezio-Catarina; Videira-Catarina; IAC.576.70-Estacao; Oriental-Estacao; Pioneira-Estacao; Salezio-Estacao; Videira-Estacao; Pioneira-IAC.576.70; Rosada-IAC.576.70; Videira-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Videira-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 440 Apronta.mesa-Amarela; Estacao-Amarela; Oriental-Amarela; Rosada-Amarela; Catarina-Apronta.mesa; IAC.576.70-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Estacao-Catarina; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; IAC.576.70-Estacao; Pioneira-Estacao; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 450 Oriental-Amarela; Rosada-Amarela; Salezio-Amarela; Catarina-Apronta.mesa; IAC.576.70-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Estacao-Catarina; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; IAC.576.70-Estacao; Pioneira-Estacao; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 300 Apronta.mesa-Amarela; IAC.576.70-Amarela; Oriental-Amarela; Pioneira-Amarela; Salezio-Amarela; Videira-Amarela; Catarina-Apronta.mesa; Estacao-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Videira-Apronta.mesa; IAC.576.70-Catarina; Oriental-Catarina; Pioneira-Catarina; Salezio-Catarina; Videira-Catarina; IAC.576.70-Estacao; Oriental-Estacao; Pioneira-Estacao; Salezio-Estacao; Videira-Estacao; Pioneira-IAC.576.70; Rosada-IAC.576.70; Videira-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Videira-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada; Videira-SalezioHierarchical Cluster Analysis
Using Euclidian Distance
uv.cassava.hc = clustering(uv.cassava.ds, method = "hc", distance = "euclidean")
dendrogram.plot(uv.cassava.ds, uv.cassava.hc)
dendrogram.plot.col(uv.cassava.ds, uv.cassava.hc, "varieties")
Principal Components Analysis
Importance of components: Proportion of Variance explained in each component
uv.cassava.pca = pca.analysis.dataset(uv.cassava.ds)
summary(uv.cassava.pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 17.129 10.087 8.289 4.249 3.0243 1.77968 1.31468
## Proportion of Variance 0.586 0.203 0.137 0.036 0.0183 0.00632 0.00345
## Cumulative Proportion 0.586 0.789 0.926 0.962 0.9802 0.98651 0.98996
## PC8 PC9 PC10 PC11 PC12 PC13
## Standard deviation 0.99687 0.84476 0.77221 0.72841 0.63003 0.47799
## Proportion of Variance 0.00198 0.00142 0.00119 0.00106 0.00079 0.00046
## Cumulative Proportion 0.99194 0.99337 0.99456 0.99562 0.99641 0.99686
## PC14 PC15 PC16 PC17 PC18 PC19
## Standard deviation 0.43455 0.41855 0.40385 0.36874 0.33393 0.32915
## Proportion of Variance 0.00038 0.00035 0.00033 0.00027 0.00022 0.00022
## Cumulative Proportion 0.99724 0.99759 0.99792 0.99819 0.99841 0.99863
## PC20 PC21 PC22 PC23 PC24 PC25
## Standard deviation 0.32294 0.3174 0.29612 0.28055 0.26165 0.25277
## Proportion of Variance 0.00021 0.0002 0.00018 0.00016 0.00014 0.00013
## Cumulative Proportion 0.99883 0.9990 0.99921 0.99937 0.99950 0.99963
## PC26 PC27 PC28 PC29 PC30
## Standard deviation 0.23929 0.2257 0.20367 0.18610 7.61e-15
## Proportion of Variance 0.00011 0.0001 0.00008 0.00007 0.00e+00
## Cumulative Proportion 0.99975 0.9999 0.99993 1.00000 1.00e+00Robust and centralized pca (3D and 2D)
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "varieties", ellipses=T)
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses=T, pallette=2)
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "varieties", ellipses="F")
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses="T", pallette=2, labels="T")
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses="T", pallette=2, labels="F")
CAROTENOIDS FINGERPRINT REGION (400-500 nm)
Carotenoids have absorption maxima in the UV-visible region of 450 nm
uv.cassava.carot = subset.x.values.by.interval(uv.cassava.ds, min.value = 400, max.value = 500)
sum.dataset(uv.cassava.carot)## Dataset summary:
## Valid dataset
## Description: UV data for cassava
## Type of data: uvv-spectra
## Number of samples: 30
## Number of data points 101
## Number of metadata variables: 3
## Label of x-axis values: wavelength(nm)
## Label of data points: absorbance
## Number of missing values in data: 0
## Mean of data values: 0.127
## Median of data values: 0.02827
## Standard deviation: 0.2668
## Range of values: -0.0228 1.498
## Quantiles:
## 0% 25% 50% 75% 100%
## -0.02280 0.01033 0.02827 0.10888 1.49789Plotting spectra
plot.spectra(uv.cassava.carot, "varieties", legend="topleft")
Principal Components Analysis
Importance of components: Proportion of Variance explained in each component
uv.cassava.carot.pca = pca.analysis.dataset(uv.cassava.carot, scale = T, center = T)
summary(uv.cassava.carot.pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 10.012 0.86277 0.1417 0.05031 0.02480 0.014 0.00975
## Proportion of Variance 0.992 0.00737 0.0002 0.00003 0.00001 0.000 0.00000
## Cumulative Proportion 0.992 0.99977 1.0000 0.99999 1.00000 1.000 1.00000
## PC8 PC9 PC10 PC11 PC12 PC13
## Standard deviation 0.00782 0.00479 0.00384 0.00307 0.003 0.00221
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.000 1.00000
## PC14 PC15 PC16 PC17 PC18 PC19
## Standard deviation 0.00207 0.00197 0.00189 0.00175 0.00169 0.00158
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC20 PC21 PC22 PC23 PC24 PC25
## Standard deviation 0.0015 0.00139 0.00133 0.00124 0.00116 0.00112
## Proportion of Variance 0.0000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.0000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC26 PC27 PC28 PC29 PC30
## Standard deviation 0.0011 0.000965 0.000929 0.000818 9.93e-16
## Proportion of Variance 0.0000 0.000000 0.000000 0.000000 0.00e+00
## Cumulative Proportion 1.0000 1.000000 1.000000 1.000000 1.00e+00PCAs Graphics
pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "varieties", ellipses="F")
pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "varieties", ellipses="T")
pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "colors", labels="F", pallette=2, ellipses="T")
Hierarchical Cluster Analysis
uv.cassava.carot.hc = clustering(uv.cassava.carot, method = "hc", distance = "euclidean")
dendrogram.plot(uv.cassava.carot, uv.cassava.carot.hc)
dendrogram.plot.col(uv.cassava.carot, uv.cassava.hc, "colors")
Profile and Quantification of Carotenoids using High Performance Liquid Chromatography (HPLC)
Subsequent analysis was performed to characterize the carotenoids by HPLC. The chromatographic analysis identified the cis-beta- and trans-beta-carotene, beta-carotene, lutein and beta-cryptoxanthin in all genotypes analyzed, confirmed the presence of lycopene only in Rosada genotype. Trans-beta-carotene was the major component in all samples.
setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
load("hplcrodolfo.RData")
hplcrodolfo## Cultivar Lutein ßCryptoxanthin aCarotene cisßcarotene Transßcarotene
## 1 Aprontamesa 0.091 0.109 0.000 0.000 0.000
## 2 Pioneira 0.319 0.071 0.306 2.967 3.425
## 3 Oriental 0.052 0.103 0.000 0.109 0.123
## 4 Amarela 0.685 0.033 0.043 3.292 4.224
## 5 Catarina 0.357 0.076 0.198 4.770 5.797
## 6 IAC57670 0.688 0.000 0.664 5.826 6.420
## 7 Salezio 0.055 0.066 0.328 0.065 0.354
## 8 Estacao 0.058 0.084 0.435 0.254 0.328
## 9 Videira 0.070 0.110 0.000 0.039 0.340
## 10 Rosada 0.511 0.605 4.732 4.480 166.296
## Lycopene
## 1 0.000
## 2 0.000
## 3 0.000
## 4 0.000
## 5 0.000
## 6 0.000
## 7 0.000
## 8 0.000
## 9 0.000
## 10 1.534cultivar=factor(hplcrodolfo$Cultivar)
cultivar## [1] Aprontamesa Pioneira Oriental Amarela Catarina
## [6] IAC57670 Salezio Estacao Videira Rosada
## 10 Levels: Amarela Aprontamesa Catarina Estacao IAC57670 ... Videirahplc<-hplcrodolfo[2:7] Apply function of ade4
require(ade4)## Loading required package: ade4## Warning: package 'ade4' was built under R version 3.1.2HPLC <- dudi.pca(hplc, center = TRUE, scale = TRUE, scan = F,nf=5)
summary(HPLC) ##summarize the function## Class: pca dudi
## Call: dudi.pca(df = hplc, center = TRUE, scale = TRUE, scannf = F,
## nf = 5)
##
## Total inertia: 6
##
## Eigenvalues:
## Ax1 Ax2 Ax3 Ax4 Ax5
## 4.2593319 1.6109364 0.1050266 0.0240342 0.0006663
##
## Projected inertia (%):
## Ax1 Ax2 Ax3 Ax4 Ax5
## 70.98887 26.84894 1.75044 0.40057 0.01111
##
## Cumulative projected inertia (%):
## Ax1 Ax1:2 Ax1:3 Ax1:4 Ax1:5
## 70.99 97.84 99.59 99.99 100.00
##
## (Only 5 dimensions (out of 6) are shown)HPLC$eig ##eigenvalues (variability in the data)## [1] 4.259e+00 1.611e+00 1.050e-01 2.403e-02 6.663e-04 4.587e-06HPLC$li ##row cordinates## Axis1 Axis2 Axis3 Axis4 Axis5
## 1 1.0560 -0.9772 0.140625 -0.118353 0.0217429
## 2 0.4634 0.5304 -0.188201 -0.015570 0.0249376
## 3 1.0947 -1.0398 -0.001810 -0.093596 -0.0219527
## 4 0.2746 1.6941 0.742037 -0.111167 -0.0098409
## 5 0.2188 1.1486 -0.653985 -0.205050 -0.0173457
## 6 -0.1705 2.4414 -0.090789 0.220190 0.0069017
## 7 1.0822 -1.0046 0.007296 0.233256 -0.0540229
## 8 0.9711 -0.9825 -0.052789 0.213374 0.0401974
## 9 1.0649 -1.0249 0.070085 -0.120416 0.0103131
## 10 -6.0551 -0.7856 0.027531 -0.002668 -0.0009305HPLC$l1 ##row normed cordinates## RS1 RS2 RS3 RS4 RS5
## 1 0.5117 -0.7699 0.433923 -0.76342 0.84230
## 2 0.2246 0.4179 -0.580727 -0.10043 0.96606
## 3 0.5304 -0.8193 -0.005586 -0.60373 -0.85043
## 4 0.1330 1.3348 2.289686 -0.71707 -0.38123
## 5 0.1060 0.9050 -2.017986 -1.32265 -0.67196
## 6 -0.0826 1.9236 -0.280147 1.42031 0.26737
## 7 0.5244 -0.7915 0.022514 1.50459 -2.09281
## 8 0.4705 -0.7741 -0.162891 1.37634 1.55722
## 9 0.5160 -0.8075 0.216261 -0.77673 0.39952
## 10 -2.9340 -0.6189 0.084953 -0.01721 -0.03605HPLC$co ##column cordinates (correlations between variables and pcs)## Comp1 Comp2 Comp3 Comp4 Comp5
## Lutein -0.4767 0.8499 0.22431 -0.007895 0.0033189
## ßCryptoxanthin -0.9231 -0.3718 -0.00572 -0.096675 0.0147035
## aCarotene -0.9830 -0.1375 -0.02382 0.119013 0.0105172
## cisßcarotene -0.5326 0.8142 -0.23026 -0.018924 -0.0006946
## Transßcarotene -0.9867 -0.1608 0.01694 -0.008404 -0.0135522
## Lycopene -0.9780 -0.2063 0.02832 -0.005738 -0.0120158HPLC$c1 ##column normed scores (loadings)## CS1 CS2 CS3 CS4 CS5
## Lutein -0.2310 0.6697 0.69216 -0.05093 0.12857
## ßCryptoxanthin -0.4473 -0.2930 -0.01765 -0.62359 0.56960
## aCarotene -0.4763 -0.1083 -0.07350 0.76768 0.40743
## cisßcarotene -0.2581 0.6415 -0.71052 -0.12207 -0.02691
## Transßcarotene -0.4781 -0.1267 0.05226 -0.05421 -0.52500
## Lycopene -0.4739 -0.1625 0.08738 -0.03701 -0.46548Plot PCA
biplot(HPLC)
scatter(HPLC,clab.row = 0, posieig = "none",col = as.numeric(cultivar))## NULLplot.new=T
s.class(HPLC$li, fac = cultivar, col =as.numeric(cultivar), add.plot = TRUE, cstar = 0, clabel = 1,
cellipse = 0,pch =23,addaxes=TRUE,cpoint=1)
Magnification (zoom) for the overlapped samples
s.class(HPLC$li, fac = cultivar, col =as.numeric(cultivar), cstar = 0, clabel = 1,
cellipse = 0,pch =23,addaxes=TRUE,cpoint=1, xlim = c(1,1.1), ylim = c(-1.1,-0.9))
Cluster Analisys (HPLC data)
Similarity of cassava genotypes in respect to their carotenoid composition determined by RP-HPLC.
setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
library(vegan)
Carotenoids_HPLC_Rodolfo <- read.table("Carotenoids_HPLC_Rodolfo.txt", header=TRUE, dec=",")
Carotenoids_HPLC_Rodolfo
# standardization of data
Carotenoids_HPLC_Rodolfo.z <- decostand(Carotenoids_HPLC_Rodolfo, "standardize", MARGIN=2)
Carotenoids_HPLC_Rodolfo.z
standard=Carotenoids_HPLC_Rodolfo.z
standard
library(clustsig)Hierarchical cluster dendogram analysis (UPGMA method) The similarity between members of the same cluster is statistically significant, when the branches in the dendrogram show the same color. Significance determined by Simprof analysis (Similiarity Profile Analysis) from R Clustsig package in accordance with Clarke, Somerfield & Gorley, (2008)
diststandard= dist(standard, method = "euclidean")
hcstandard=hclust(diststandard)
plot(hcstandard)
Carotenoids_HPLC_RodolfoSIMPROF <- simprof(data=Carotenoids_HPLC_Rodolfo, method.distance="euclidean")
simprof.plot(Carotenoids_HPLC_RodolfoSIMPROF) 
## 'dendrogram' with 2 branches and 10 members total, at height 164.1